Characterization of Balance Control and Postural Stability in Patients With Alzheimer Disease.

Balance in Alzheimer disease (AD) patients is not rigorously understood. In this study, we characterize balance using qualitative [Berg Balance Scale (BBS)] and quantitative measures (posturography) and assess relationships between qualitative and quantitative balance measures in AD. Patients with mild-moderate AD (n=48) were recruited. BBS scores and posturography metrics, including medial-lateral sway range, anterior-posterior sway range, sway area, and sway velocity, were assessed in eyes-open and eyes-closed conditions. Adjusted linear regressions were used to assess relationships between posturography and BBS score. Mean BBS score was 50.4±5.3. In eyes-open conditions, posturography and BBS score were not significantly associated. In eyes-closed conditions, better BBS score was significantly associated with lower sway area (ß=-0.91; P =0.006). Better scores of BBS items involving turning and reduced base of support were associated with greater eyes-closed sway area. Posturography in the more challenging eyes-closed condition may predict functional balance deficits in AD patients.

Computational analysis of LDDMM for brain mapping.

One goal of computational anatomy (CA) is to develop tools to accurately segment brain structures in healthy and diseased subjects. In this paper, we examine the performance and complexity of such segmentation in the framework of the large deformation diffeomorphic metric mapping (LDDMM) registration method with reference to atlases and parameters. First we report the application of a multi-atlas segmentation approach to define basal ganglia structures in healthy and diseased kids' brains. The segmentation accuracy of the multi-atlas approach is compared with the single atlas LDDMM implementation and two state-of-the-art segmentation algorithms-Freesurfer and FSL-by computing the overlap errors between automatic and manual segmentations of the six basal ganglia nuclei in healthy subjects as well as subjects with diseases including ADHD and Autism. The high accuracy of multi-atlas segmentation is obtained at the cost of increasing the computational complexity because of the calculations necessary between the atlases and a subject. Second, we examine the effect of parameters on total LDDMM computation time and segmentation accuracy for basal ganglia structures. Single atlas LDDMM method is used to automatically segment the structures in a population of 16 subjects using different sets of parameters. The results show that a cascade approach and using fewer time steps can reduce computational complexity as much as five times while maintaining reliable segmentations.